Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants

The selective catalytic reduction (SCR) de-NOx process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NOx analysis; hence, it is difficult to obtain an accurate model that can be used to control NH3 injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NOx emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NOx process. Through the analysis of the experimental field data in the steady state, the variable state and the NOx analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NOx emission.